Legal claims defining the scope of protection, as filed with the USPTO.
1. A computer-implemented method for removing noise from a signal of a target object, the computer-implemented method comprising: receiving an input signal from a source system, the input signal comprising image data identifying a target object and a plurality of parameters, and further comprising a noise portion, wherein the image data is selected from a group consisting of X-ray, CT, emission tomography, SPECT and PET, and the target object comprises image data of a body part of a patient; selecting an initial subset of parameters from the plurality of parameters; estimating a nonparametric probability distribution function (pdf) from the received input signal which comprises a linear combination of functions; generating a quadratic likelihood function (QLF) based on the nonparametric PDF estimated including the linear combination of functions; determining a fit of the initially selected subset of parameters to the data identifying the target object, based on the QLF; in response to the determined fit of the initially selected subset of parameters being below a predetermined threshold, iteratively optimizing parameters by selecting a new subset of parameters from the plurality of parameters and determining a fit of the new subset of parameters, until the determined fit satisfies the predetermined threshold; and generating an output signal comprising a reconstructed signal of the target object constructed using the new subset of iteratively optimized parameters.
2. The computer-implemented method of claim 1 , wherein the data comprises weights w i and the QLF has the form L = ∫ d x [ 1 2 f ( x ) 2 - f ( x ) ∑ i w i δ ( x - x i ) ] .
3. The computer-implemented method of claim 1 , further comprising calculating a source term using the data and basis functions.
4. The computer-implemented method of claim 3 , wherein the QLF is obtained by: computing a Gram matrix using the basis functions; and combining the Gram matrix, the parameters and the source tem′ to produce the QLF.
5. The computer-implemented method of claim 1 , wherein the output comprises a 2D, 3D, or 4D image representation of the target object displayed at a graphical user interface.
6. The computer-implemented method of claim 1 , wherein the image data is taken in at least two planes, and wherein the output comprises a 3D representation.
7. The computer-implemented method of claim 1 , wherein the image data is taken in a least two planes and further comprises time, and wherein the output comprises a 4D representation.
8. A system for modeling of data describing a target object contained in an input signal, the system comprising: a computer-readable medium; a parameter optimization processor coupled to the computer-readable medium; and a communication interface coupled to the parameter optimization processor and adapted to receive and transmit electronic representations of reconstructed models signals to and from the parameter optimization processor, respectively, the computer-readable medium having stored thereon software instructions that, when executed by the parameter optimization processor, cause the parameter optimization processor to perform operations including: receive the input signal from a source system configured to collect object data, the input signal comprising image data identifying a target object and a plurality of parameters, and further comprises noise, wherein the image data is selected from a group consisting of X-ray, CT, emission tomography, SPECT and PET, and the target object comprises image data of a body part of a patient; select an initial subset of parameters corresponding to the target object from the plurality of parameters; estimate a nonparametric probability distribution function comprising (pdf) from the received input signal which comprises a linear combination of functions; generate a quadratic likelihood function (QLF) based on the nonparametric PDF including the linear combination of functions; determine a fit of the initially selected subset of parameters to the data identifying the target object, based on the QLF; in response to the determined fit of the initially selected subset of parameters being below a predetermined threshold, iteratively optimize parameters by selecting a new subset of parameters from the plurality of parameters and determining a fit of the new subset of parameters, until the determined fit satisfies the predetermined threshold; and generate an output signal comprising a signal of the target object constructed using the new subset of iteratively optimized parameters.
9. The system of claim 8 , wherein the data comprises weights w; and the QLF has the form L = ∫ d x [ 1 2 f ( x ) 2 - f ( x ) ∑ i w i δ ( x - x i ) ] .
10. The system of claim 8 , further comprising calculating a source term using the data and basis functions.
11. The system of claim 10 , wherein the QLF is obtained by: computing a Gram matrix using the basis functions; and combining the Gram matrix, the parameters and the source term to produce the QLF.
12. The system of claim 8 , wherein the output signal comprises a 2D, 3D or 4D image representation of the target object displayed at a graphical user interface.
13. The system of claim 8 , wherein the image data is taken in at least two planes, and wherein the output comprises a 3D representation.
14. The system of claim 8 , wherein the image data is taken in a least two planes and further comprises time, and wherein the output comprises a 4D representation.
15. A method of generating a reconstructed image of a target object from an input signal having a data component and a noise component, the method comprising: receiving the input signal from an image source system, the input signal comprising image data identifying a target object and a plurality of parameters, and further comprising a noise portion, wherein the image data is selected from a group consisting of X-ray, CT, emission tomography, SPECT and PET, and the target object comprises image data of a body part of a patient; selecting an initial subset of parameters from the plurality of parameters; estimating a nonparametric probability distribution function (pdf) from the received input signal which comprises a linear combination of functions; generating a quadratic likelihood function (QLF) based on the nonparametric PDF including the linear combination of functions; determining a fit of the initially selected subset of parameters to the data identifying the target object, based on the QLF; in response to the determined fit of the initially selected subset of parameters being below a predetermined threshold, iteratively optimizing parameters by selecting a new subset of parameters from the plurality of parameters and determining a fit of the new subset of parameters, until the determined fit satisfies the predetermined threshold; and generating an output signal comprising a display of reconstructed image of the target object based on the new subset of iteratively optimized parameters.
16. The method of claim 15 , wherein the input signal comprises first plane image data and second plane image data, and the output comprises displaying a three-dimensional image of the target object.
17. The method of claim 16 , wherein the data comprises weights w; and the QLF has the form L = ∫ d x [ 1 2 f ( x ) 2 - f ( x ) ∑ i w i δ ( x - x i ) ] .
18. The method of claim 15 , further comprising calculating a source term using the data and basis functions.
19. The method of claim 18 , wherein the QLF is obtained by: computing a Gram matrix using the basis functions; and combining the Gram matrix, the parameters and the source term to produce the QLF.
20. The computer-implemented method of claim 1 , wherein the QDF comprises a form L = ∫ d x [ 1 2 f ( x , θ ) 2 - f ( x , θ ) ∑ i δ ( x - x i ) ] where θ represents the parameters, x represents the positions of the observations, and f (x, θ) is the pdf.
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September 4, 2018
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